{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2a006bea",
   "metadata": {},
   "source": [
    "# Get resource from google scholar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d89bba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the requests library \n",
    "import requests \n",
    "import os\n",
    "from bs4 import BeautifulSoup\n",
    "path = os.getcwd()\n",
    "keyword = 'obesity' \n",
    "# insert your auth key here, you need a proxies account and get free api calls\n",
    "auth_key = \"040d202dffa14fe75209dac7b2a872a1_sr98766_ooPq87\"\n",
    "URL = \"http://api.proxiesapi.com\"\n",
    "for i in range(0,110,10):\n",
    "    url = \"https://scholar.google.com/scholar?start={0}&q={1}&hl=en&as_sdt=8,5\".format(i,keyword)\n",
    "    # defining a params dict for the parameters to be sent to the API \n",
    "    PARAMS = {'auth_key':auth_key, 'url':url} \n",
    "    # sending get request and saving the response as response object \n",
    "    r = requests.get(url = URL, params = PARAMS)\n",
    "    #make soup to get title from google scholar html\n",
    "    soup = BeautifulSoup(r.content,'lxml')\n",
    "    #save the results to txt file and name it as {keyword}.txt \n",
    "    with open(path + \"/{0}.txt\".format(keyword), 'a+') as f:\n",
    "        #title part\n",
    "        for item in soup.select('[data-clk-atid]'):\n",
    "            # get rid of [pdf] items\n",
    "            if item.get_text().startswith('['):\n",
    "                continue\n",
    "            else:\n",
    "                f.write(item.get_text())\n",
    "                f.write('\\n')\n",
    "                print(item.get_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54d80bf",
   "metadata": {},
   "source": [
    "## The data is to crawl the titles of articles in the cancer and obesity categories of google scholar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aded62c1",
   "metadata": {},
   "source": [
    "# Load data from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f40ed224",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mixed_data = []\n",
    "with open('obesity.txt','r',encoding='utf-8') as f:\n",
    "    for l in f:\n",
    "        mixed_data.append(l.strip('\\n'))\n",
    "with open('cancer.txt','r',encoding='utf-8') as f:\n",
    "    for l in f:\n",
    "        mixed_data.append(l.strip('\\n'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cda6c47",
   "metadata": {},
   "source": [
    "## The mixed_data list will hold all the corpus, the first 100 items are belong to obesity, the left will be cancer category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "2e8212a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Obesity and the environment: where do we go from here?', 'Obesity as a medical problem', 'Medical consequences of obesity', 'The epidemiology of obesity', 'Focus on Obesity', 'The practical guide: identification, evaluation, and treatment of overweight and obesity in adults', 'PPARs and the complex journey to obesity', 'The medical risks of obesity', 'The epidemiology of obesity', 'Social factors in obesity', 'The obesity crisis', 'Economic causes and consequences of obesity', 'Obesity and severe obesity forecasts through 2030', 'Obesity epidemiology', 'Causes of obesity', 'The obesity epidemic: pathophysiology and consequences of obesity', 'Update on obesity', 'Medical hazards of obesity', 'The epidemiology of obesity', 'An anthropological perspective on obesity', 'Health consequences of obesity', 'Who pays for obesity?', 'Obesity and the Economics of Prevention', 'Childhood obesity', 'Surgery for obesity', 'Increases in morbid obesity in the USA: 2000–2005', 'Complications of obesity', 'Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report', 'The worldwide obesity epidemic', 'Obesity as a disease', 'Inflammatory mechanisms in obesity', 'Obesity in the new millennium', 'Obesity and infection', 'Environmental contributions to the obesity epidemic', 'Apparatus and methods for treatment of morbid obesity', 'Obesity is an environmental issue', 'Obesity: why be concerned?', 'Method for treatment of morbid obesity', 'Risks of obesity', 'Obesity statistics', 'Prevalence of overweight and obesity in the United States, 1999-2004', 'Method and appartus for treating obesity', \"New criteria for'obesity disease'in Japan\", 'Quality of life and obesity', 'Obesity and GERD', 'The effect of obesity on health outcomes', 'Obesity in Saudi Arabia.', 'Obesity', 'Obesity: the disease of the twenty-first century', 'Pathways to obesity', 'Predicting obesity in young adulthood from childhood and parental obesity', 'The medical complications of obesity', 'Obesity paradoxes', 'Obesity as a disease', 'Pathophysiology of obesity.', 'Method for treating morbid obesity', 'Pathophysiology of obesity', 'Obesity in the elderly', 'Childhood obesity: the health issue', 'Obesity in women from developing countries', 'Health implications of obesity', 'Current views on obesity', 'Eotaxin and obesity', 'Obesity and insulin resistance', 'The cost of obesity in Canada', 'Prevalence of obesity in the United States', 'Childhood obesity', 'The cost of obesity', 'Devices and methods for treating morbid obesity', 'The epidemic of obesity', 'Obesity—United States, 1988–2008', 'Obesity in Malaysia', 'Childhood obesity: causes and consequences', 'Apparatus and methods for treatment of morbid obesity', 'Overweight and obesity in the United States: prevalence and trends, 1960–1994', 'The Asia-Pacific perspective: redefining obesity and its treatment', 'The economic impact of obesity in the United States', 'Fetal origins of obesity', 'Understanding overeating and obesity', 'Obesity in America: A conference', 'The influence of obesity on health', 'Is obesity an inflammatory condition?', 'Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001', 'Obesity: preventing and managing the global epidemic', 'Bias, discrimination, and obesity', 'Minisymposium on obesity: overview and some strategic considerations', 'Socioeconomic status and obesity', 'Critical periods in childhood for the development of obesity', 'Socioeconomic status and obesity: a review of the literature.', 'Obesity: a growing problem', 'The economics of obesity', 'The psychosomatic concept of obesity.', 'Obesity—United States, 1999–2010', 'Gender, obesity, and education', 'Does hunger cause obesity?', 'Workshop on childhood obesity: summary of the discussion', 'Assessment of child and adolescent overweight and obesity', 'Obesity on campus', 'An adoption study of human obesity', 'Health effects of overweight and obesity in 195 countries over 25 years', 'The immunobiology of cancer immunosurveillance and immunoediting', 'The epigenomics of cancer', 'Cancer undefeated', 'Phyto-oestrogens and cancer', 'The microcosmos of cancer', 'The three Es of cancer immunoediting', 'Cancer micrometastases', 'How cancer arises', 'Gastic cancer', 'The “cancer immunogram”', 'MiRNAs and cancer', 'Sunlight and cancer', 'Cyclins and cdks in development and cancer: a perspective', 'The war on cancer.', 'Cyclooxygenases in cancer: progress and perspective', 'Kinesins and cancer', 'Papillomaviruses and cancer: from basic studies to clinical application', 'Galectins and cancer', 'Collagenases in cancer', 'Cyclins and cancer', 'Cancer despite immunosurveillance: immunoselection and immunosubversion', 'The biology of cancer', 'Rethinking cancer nanotheranostics', 'The politics of cancer', 'The age of cancer', 'Statistical methods in cancer research', 'The new era in cancer research', 'Cancer issue: why cancer and inflammation?', 'Cancer without disease', 'Breast cancer', 'Evidence for the involvement of interleukin 6 in experimental cancer cachexia.', 'Breast cancer', 'The causes and prevention of cancer', 'The family with cancer', 'Anthracyclines in the treatment of cancer', 'Biochemistry of cancer', 'On the origin of cancer cells', 'Cancer immunology', 'The molecular genetics of cancer', 'Diabetes and cancer', 'Cancer modelling and simulation', 'Breast cancer treatment: a review', 'Antioncogenes and human cancer', 'Enzymology of cancer cells', 'Cancer trends in China', 'Cadherins and catenins in breast cancer', 'The emerging role of lncRNAs in cancer', 'Cancer in Africa', 'Prostate cancer', 'miRNAs in human cancer', 'Characterization of a cancer cachectic factor', 'Focus on lung cancer', 'Teratologies: A cultural study of cancer', 'The hallmarks of cancer', 'The National Cancer Institute: cancer drug discovery and development program.', 'Recent cancer trends in the United States', 'Stress and cancer.', \"Understanding the cancer patient's search for meaning.\", 'Apoptosis in cancer', 'Pancreatic cancer', 'The cancer genome', 'Esophageal cancer', 'Cancer cell cycles', 'A report on the natural duration of cancer.', 'The molecular biology of cancer', 'Screening for breast cancer', 'Interferons, immunity and cancer immunoediting', 'Pancreatic cancer', 'Cancer of the colon', 'The role of cyclooxygenases in inflammation, cancer, and development', 'Reneotiating identity: cancer narratives', 'Epidemiology of breast cancer', 'Pancreatic cancer', 'Colon cancer', 'Cancer statistics, 2015.', 'The role of iron in cancer.', 'Nanocarriers as an emerging platform for cancer therapy', 'Fibroblasts in cancer', 'Cancer statistics, 2014.', 'The promise of retinoids to fight against cancer', 'Cancer burden in the year 2000. The global picture', 'Cancer nursing', 'Tea and cancer', 'Cytokines in cancer pathogenesis and cancer therapy', 'A new theory on the cancer-inducing mechanism', 'Cancer genes and the pathways they control', 'Papillomaviruses in human cancer.', 'The emperor of all maladies: a biography of cancer', 'Ca 2+ signalling checkpoints in cancer: remodelling Ca 2+ for cancer cell proliferation and survival', 'Cells of origin in cancer', 'Heat shock protein 90: the cancer chaperone', 'Cancer as an evolutionary and ecological process', 'Cancer statistics, 2005.', 'Prophylactic cranial irradiation is indicated following complete response to induction therapy in small cell lung cancer: results of a multicentre randomised trial', 'Retinoids in cancer therapy.', 'Chemotherapy and the war on cancer', 'Gastric cancer', 'A change of strategy in the war on cancer', 'Coping with cancer', 'Inflammation and cancer']\n"
     ]
    }
   ],
   "source": [
    "print(mixed_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0c8190",
   "metadata": {},
   "source": [
    "# Make labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "59fa4727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "labels = np.array([0]*200)\n",
    "labels[100:200]=1\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34422118",
   "metadata": {},
   "source": [
    "## labels are used to generate plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ea6649",
   "metadata": {},
   "source": [
    "# Tokenizing text with scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "477548c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(stop_words='english')\n",
    "mixed_counts = vectorizer.fit_transform(mixed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "7a910c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<200x314 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 672 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925d50c9",
   "metadata": {},
   "source": [
    "# From occurrences to frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "cbd0b9f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "mixed_tfidf = tfidf_transformer.fit_transform(mixed_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "883b82a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 314)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_tfidf.toarray().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "d3c6dda4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['195', '1960', '1988', '1994', '1999', '2000', '2001', '2004', '2005', '2008', '2010', '2014', '2015', '2030', '25', '90', 'adolescent', 'adoption', 'adulthood', 'adults', 'africa', 'age', 'america', 'anthracyclines', 'anthropological', 'antioncogenes', 'apoptosis', 'apparatus', 'appartus', 'application', 'arabia', 'arises', 'asia', 'assessment', 'basic', 'bias', 'biochemistry', 'biography', 'biology', 'breast', 'burden', 'ca', 'cachectic', 'cachexia', 'cadherins', 'campus', 'canada', 'cancer', 'catenins', 'cause', 'causes', 'cdks', 'cell', 'cells', 'century', 'change', 'chaperone', 'characterization', 'checkpoints', 'chemotherapy', 'child', 'childhood', 'china', 'clinical', 'collagenases', 'colon', 'complete', 'complex', 'complications', 'concept', 'concerned', 'condition', 'conference', 'consequences', 'considerations', 'contributions', 'control', 'coping', 'cost', 'countries', 'cranial', 'crisis', 'criteria', 'critical', 'cultural', 'current', 'cycles', 'cyclins', 'cyclooxygenases', 'cytokines', 'despite', 'developing', 'development', 'devices', 'diabetes', 'discovery', 'discrimination', 'discussion', 'disease', 'does', 'drug', 'duration', 'ecological', 'economic', 'economics', 'education', 'effect', 'effects', 'elderly', 'emerging', 'emperor', 'environment', 'environmental', 'enzymology', 'eotaxin', 'epidemic', 'epidemiology', 'epigenomics', 'era', 'es', 'esophageal', 'evaluation', 'evidence', 'evolutionary', 'experimental', 'factor', 'factors', 'family', 'fetal', 'fibroblasts', 'fight', 'focus', 'following', 'forecasts', 'galectins', 'gastic', 'gastric', 'gender', 'genes', 'genetics', 'genome', 'gerd', 'global', 'growing', 'guide', 'guidelines', 'hallmarks', 'hazards', 'health', 'heat', 'human', 'hunger', 'identification', 'identity', 'immunity', 'immunobiology', 'immunoediting', 'immunogram', 'immunology', 'immunoselection', 'immunosubversion', 'immunosurveillance', 'impact', 'implications', 'increases', 'indicated', 'inducing', 'induction', 'infection', 'inflammation', 'inflammatory', 'influence', 'institute', 'insulin', 'interferons', 'interleukin', 'involvement', 'iron', 'irradiation', 'issue', 'japan', 'journey', 'kinesins', 'life', 'literature', 'lncrnas', 'lung', 'maladies', 'malaysia', 'managing', 'meaning', 'mechanism', 'mechanisms', 'medical', 'method', 'methods', 'microcosmos', 'micrometastases', 'millennium', 'minisymposium', 'mirnas', 'modelling', 'molecular', 'morbid', 'multicentre', 'nanocarriers', 'nanotheranostics', 'narratives', 'national', 'natural', 'new', 'nursing', 'obesity', 'oestrogens', 'origin', 'origins', 'outcomes', 'overeating', 'overview', 'overweight', 'pacific', 'pancreatic', 'papillomaviruses', 'paradoxes', 'parental', 'pathogenesis', 'pathophysiology', 'pathways', 'patient', 'pays', 'periods', 'perspective', 'phyto', 'picture', 'platform', 'politics', 'ppars', 'practical', 'predicting', 'prevalence', 'preventing', 'prevention', 'problem', 'process', 'program', 'progress', 'proliferation', 'promise', 'prophylactic', 'prostate', 'protein', 'psychosomatic', 'quality', 'randomised', 'recent', 'redefining', 'related', 'remodelling', 'reneotiating', 'report', 'research', 'resistance', 'response', 'results', 'rethinking', 'retinoids', 'review', 'risk', 'risks', 'role', 'saudi', 'screening', 'search', 'severe', 'shock', 'signalling', 'simulation', 'small', 'social', 'socioeconomic', 'states', 'statistical', 'statistics', 'status', 'strategic', 'strategy', 'stress', 'studies', 'study', 'summary', 'sunlight', 'surgery', 'survival', 'tea', 'teratologies', 'theory', 'therapy', 'treating', 'treatment', 'trends', 'trial', 'undefeated', 'understanding', 'united', 'update', 'usa', 'views', 'war', 'women', 'workshop', 'worldwide', 'year', 'years', 'young']\n"
     ]
    }
   ],
   "source": [
    "print(vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f76396",
   "metadata": {},
   "source": [
    "# Fit tfidf vector to PCA algorithmn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "5d88a7fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca_matrix = pca.fit_transform(mixed_tfidf.todense())\n",
    "plt.scatter(pca_matrix[:, 0], pca_matrix[:, 1], c=labels, cmap=plt.cm.Spectral)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a82759",
   "metadata": {},
   "source": [
    "# Fit tfidf vector to TSNE algorithmn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "42dfd09c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "embeddings = TSNE(n_components=2)\n",
    "tsne_matrix = embeddings.fit_transform(mixed_tfidf.todense())\n",
    "plt.scatter(tsne_matrix[:, 0], tsne_matrix[:, 1], c=labels, cmap=plt.cm.Spectral)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1aab50",
   "metadata": {},
   "source": [
    "# Fit tfidf vector to UMAP algorithmn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "08ae61d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from umap import umap_\n",
    "import umap.plot\n",
    "umap_matrix = umap_.UMAP(metric='hellinger').fit(mixed_tfidf.todense())\n",
    "umap.plot.points(umap_matrix, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d26b7e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
